The current “smart grid” initiative using “smart meters” is driving appliance and equipment manufacturers to provide connectivity that can respond to and control the peak energy loads in residential and business settings. Smart meters raise consumer awareness of the cost and impact of electric devices. Such devices typically utilize ZigBee® communication protocol for wireless home area networks relying upon different energy profiles to deliver both energy and other information. As ZigBee® is a relatively low data rate wireless protocol that does not support streaming of content or downloads for remote software updates and applications, WiFi is used complementarily.
For instance, ZigBee is a wireless language connecting different devices to work together. Available ZigBee networks provide a suggested standard for deploying switches, sensors, and controllers using harvested energy in residential, commercial and, industrial environments. The ZigBee networks include a physical radio using IEEE 802.15.4 standard radios operating in a 2.4 GHz band. The proposed ZigBee standard seeks to provide interference avoidance, mesh networking, security, certification, and an open standard.
The emerging Smart Grid provides residential users flexibility in controlling their electricity costs. A primary driving force is the smart meter which can deliver near real-time electricity prices to homes. The consumer can make use of this information via an in-home energy management controller, which uses prices and user preferences to control power usage across the home. The energy management unit, which may be standalone or embedded in the smart meter or appliance.
The problem observed with the Smart Grid unit is that there is a “rebound” effect wherein the “scheduled” energy management includes more peaks than the “non-scheduled” solution, thereby negating benefits of off-peak pricing models. For example, FIG. 1 shows a prior art simulation of an identified “rebound” problem that occurs at the end of a peak pricing period as identified by L. Snyder, S. Kishore, “Control Mechanisms for Residential Electricity Demand in Smart Grids”, IEEE Smart Grid Communications Conference, October 2010. Specifically, FIG. 1 illustrates a plot 2 of the total power consumption of a simulated fifty home community over a three day period. As noted, a utility company may provide information regarding various peak pricing periods 4 throughout a predetermined time period. The consumer can make use of this information via the in-home energy management controller (EMC) to delay power usage until the end of the peak pricing period 4.
Although the scheduled system reduces the peak demand to a small extent, it also creates a new, even larger “rebound” peak 6, immediately after the peak pricing period 4 ends and off-peak prices begin. As illustrated, the maximum load is larger in the scheduled system than in the non-scheduled system (83.2 vs. 68.0 kW), as is the standard deviation of the load across periods (13.2 vs. 11.3 kW). This rebound peak 6 occurs even though the request intensity during off-peak hours is low. Therefore, it is clear that the off-peak pricing model fails to achieve its goal of reducing load peaks, and may even worsen the problem.
To overcome the “rebound” peak 6, the previous system proposed a complex power scheduling protocol in a Smart Grid system, as well as two optimization methods for choosing the timing of appliance operation within a home. The authors distributed scheduling mechanism guarantees homes a base power level while allowing them to compete for the remaining available power. The acknowledged problem being that the proposed optimization methods are complex in nature and do not work for all power grid configurations.